[Generation, Gemma 3] When passing a custom generation_config, overwrite default values with the model's base generation_config

src/transformers/generation/configuration_utils.py

@@ -73,7 +73,7 @@
     }
     QUANT_BACKEND_CLASSES_MAPPING = {"quanto": QuantoQuantizedCache, "HQQ": HQQQuantizedCache}
     ALL_CACHE_IMPLEMENTATIONS = (
-        list(NEED_SETUP_CACHE_CLASSES_MAPPING.keys()) + list(CACHE_CONFIG_MAPPING.keys()) + ["offloaded"]
+        list(NEED_SETUP_CACHE_CLASSES_MAPPING.keys()) + list(CACHE_CONFIG_MAPPING.keys()) + ["offloaded", "dynamic"]
     )
 
 
@@ -175,16 +175,16 @@ class GenerationConfig(PushToHubMixin):
         cache_implementation (`str`, *optional*, default to `None`):
             Name of the cache class that will be instantiated in `generate`, for faster decoding. Possible values are:
 
+            - `"dynamic"`: [`DynamicCache`]
             - `"static"`: [`StaticCache`]
             - `"offloaded_static"`: [`OffloadedStaticCache`]
             - `"sliding_window"`: [`SlidingWindowCache`]
             - `"hybrid"`: [`HybridCache`]
             - `"mamba"`: [`MambaCache`]
             - `"quantized"`: [`QuantizedCache`]
 
-            We support other cache types, but they must be manually instantiated and
-            passed to `generate` through the `past_key_values` argument. See our
-            [cache documentation](https://huggingface.co/docs/transformers/en/kv_cache) for further information.
+            If none is specified, we will use the default cache for the model (which is often [`DynamicCache`]). See
+            our [cache documentation](https://huggingface.co/docs/transformers/en/kv_cache) for further information.
         cache_config (`CacheConfig` or `dict`, *optional*, default to `None`):
             Arguments used in the key-value cache class can be passed in `cache_config`. Can be passed as a `Dict` and
             it will be converted to its repsective `CacheConfig` internally.

src/transformers/generation/utils.py

@@ -1177,21 +1177,37 @@ def _merge_criteria_processor_list(
         default_list: Union[LogitsProcessorList, StoppingCriteriaList],
         custom_list: Union[LogitsProcessorList, StoppingCriteriaList],
     ) -> Union[LogitsProcessorList, StoppingCriteriaList]:
+        """
+        Merge user-defined processors/criteria with the ones instantiated inside `generate`. In case the same
+        processor/criteria is present on both lists, use the user-defined one.
+
+        (Note: up to v4.49.0, this funtion threw an exception is the same logit processor was found twice.)
+        """
         if len(custom_list) == 0:
             return default_list
+
+        final_list = type(default_list)()
         for default in default_list:
+            using_custom = False
             for custom in custom_list:
                 if type(custom) is type(default):
                     object_type = "stopping criteria" if isinstance(custom, StoppingCriteria) else "logits processor"
-                    raise ValueError(
-                        f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
-                        f" `.generate()`, but it has already been created with the values {default}. {default} has been"
-                        " created by passing the corresponding arguments to generate or by the model's config default"
-                        f" values. If you just want to change the default values of {object_type} consider passing"
-                        f" them as arguments to `.generate()` instead of using a custom {object_type}."
+                    logger.warning_once(
+                        f"A custom {object_type} of type {type(custom)} has been passed to `.generate()`, but it "
+                        f"was also created in `.generate()`, given its parameterization. The custom {type(custom)} "
+                        f"will take precedence. Please check the docstring of {type(custom)} to see related "
+                        "`.generate()` flags."
                     )
-        default_list.extend(custom_list)
-        return default_list
+                    final_list.append(custom)
+                    using_custom = True
+                    break
+            if not using_custom:
+                final_list.append(default)
+
+        for custom in custom_list:
+            if custom not in final_list:
+                final_list.append(custom)
+        return final_list
 
     def compute_transition_scores(
         self,
@@ -1573,17 +1589,28 @@ def _prepare_generation_config(
         # exception will be raised in `_validate_model_kwargs`
         if not is_torchdynamo_compiling():
             generation_config = copy.deepcopy(generation_config)
-            model_kwargs = generation_config.update(**kwargs)
-            # If `generation_config` is provided, let's fallback ALL special tokens to the default values for the model
+
+            # If `generation_config` is provided, let's fallback ALL default values to the model's generation config
+            # TODO (joao): per-model generation config classes.
             if not using_model_generation_config:
-                if generation_config.bos_token_id is None:
-                    generation_config.bos_token_id = self.generation_config.bos_token_id
-                if generation_config.eos_token_id is None:
-                    generation_config.eos_token_id = self.generation_config.eos_token_id
-                if generation_config.pad_token_id is None:
-                    generation_config.pad_token_id = self.generation_config.pad_token_id
-                if generation_config.decoder_start_token_id is None:
-                    generation_config.decoder_start_token_id = self.generation_config.decoder_start_token_id
+                modified_values = {}
+                default_generation_config = GenerationConfig()
+                for key, default_value in default_generation_config.__dict__.items():
+                    if key.startswith("_"):  # metadata
+                        continue
+                    custom_gen_config_value = getattr(generation_config, key)
+                    model_gen_config_value = getattr(self.generation_config, key)
+                    if custom_gen_config_value == default_value and model_gen_config_value != default_value:
+                        modified_values[key] = model_gen_config_value
+                        setattr(generation_config, key, model_gen_config_value)
+                if len(modified_values) > 0:
+                    logger.warning_once(
+                        f"`generation_config` default values have been modified to match model-specific defaults: "
+                        f"{modified_values}. If this is not desired, please set these values explicitly."
+                    )
+
+            # Finally, apply any passed kwargs
+            model_kwargs = generation_config.update(**kwargs)
         else:
             model_kwargs = kwargs
 
@@ -1837,6 +1864,8 @@ def _prepare_cache_for_generation(
                 model_kwargs[cache_name] = cache_class(cache_config)
             elif generation_config.cache_implementation == "offloaded":
                 model_kwargs[cache_name] = OffloadedCache()
+            elif generation_config.cache_implementation == "dynamic":
+                model_kwargs[cache_name] = DynamicCache()
 
         # Use DynamicCache() instance by default. This will avoid back and forth from legacy format that
         # keeps copying the cache thus using much more memory

tests/generation/test_utils.py

@@ -1162,8 +1162,8 @@ def test_beam_search_low_memory(self):
             # The two outputs must match and their shape must be as expected
             self._check_similar_generate_outputs(low_output, high_output)
 
-    @pytest.mark.generate
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         # This test ensures that the assisted generation does not introduce output changes over greedy search.
         # See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535 for more info.

tests/models/aya_vision/test_modeling_aya_vision.py

@@ -16,6 +16,7 @@
 
 import unittest
 
+import pytest
 from parameterized import parameterized
 
 from transformers import (
@@ -261,6 +262,7 @@ def test_eager_matches_sdpa_generate(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -269,6 +271,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass

tests/models/cohere2/test_modeling_cohere2.py

@@ -16,6 +16,7 @@
 
 import unittest
 
+import pytest
 from packaging import version
 from parameterized import parameterized
 from pytest import mark
@@ -81,6 +82,7 @@ def test_eager_matches_sdpa_generate(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -89,6 +91,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass

tests/models/fuyu/test_modeling_fuyu.py

@@ -299,12 +299,13 @@ def test_training_gradient_checkpointing_use_reentrant(self):
     def test_training_gradient_checkpointing_use_reentrant_false(self):
         pass
 
-    @pytest.mark.generate
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Fuyu doesn't support assisted generation due to the need to crop/extend image patches indices")
     def test_assisted_decoding_matches_greedy_search(self):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Fuyu doesn't support assisted generation due to the need to crop/extend image patches indices")
     def test_assisted_decoding_sample(self):
         pass

tests/models/gemma2/test_modeling_gemma2.py

@@ -16,6 +16,7 @@
 
 import unittest
 
+import pytest
 from packaging import version
 from parameterized import parameterized
 from pytest import mark
@@ -96,6 +97,7 @@ def test_eager_matches_sdpa_generate(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Gemma2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -104,6 +106,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Gemma2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass

tests/models/gemma3/test_modeling_gemma3.py

@@ -16,13 +16,15 @@
 
 import unittest
 
+import pytest
 from parameterized import parameterized
 
 from transformers import (
     AutoModelForCausalLM,
     AutoTokenizer,
     Gemma3Config,
     Gemma3TextConfig,
+    GenerationConfig,
     is_torch_available,
 )
 from transformers.testing_utils import (
@@ -75,6 +77,7 @@ def test_model_outputs_equivalence(self, **kwargs):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -83,6 +86,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass
@@ -277,6 +281,7 @@ def test_model_outputs_equivalence(self, **kwargs):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -285,6 +290,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Gemma3 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass
@@ -551,3 +557,34 @@ def test_generation_beyond_sliding_window(self, attn_implementation: str):
 
         EXPECTED_COMPLETIONS = [" and I'm going to take a walk.\n\nI really enjoy the scenery, and I'", ", green, yellow, orange, purple, brown, black, white, gray.\n\nI'"]  # fmt: skip
         self.assertEqual(output_text, EXPECTED_COMPLETIONS)
+
+    def test_generation_beyond_sliding_window_with_generation_config(self):
+        """
+        Same as `test_generation_beyond_sliding_window`, but passing a GenerationConfig. Regression test for #36684 --
+        ensures `cache_implementation='hybrid'` is correctly inherited from the base `model.generation_config`.
+        """
+        model_id = "gg-hf-g/gemma-3-1b-it"
+        attn_implementation = "sdpa"
+
+        input_text = [
+            "This is a nice place. " * 800 + "I really enjoy the scenery,",  # This is larger than 4096 tokens
+            "A list of colors: red, blue",  # This will almost all be padding tokens
+        ]
+        tokenizer = AutoTokenizer.from_pretrained(model_id, padding="left")
+        inputs = tokenizer(input_text, padding=True, return_tensors="pt").to(torch_device)
+
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id, attn_implementation=attn_implementation, torch_dtype=torch.float16
+        ).to(torch_device)
+
+        # Make sure prefill is larger than sliding window
+        input_size = inputs.input_ids.shape[-1]
+        self.assertTrue(input_size > model.config.sliding_window)
+
+        generation_config = GenerationConfig(max_new_tokens=20)
+
+        out = model.generate(**inputs, generation_config=generation_config)[:, input_size:]
+        output_text = tokenizer.batch_decode(out)
+
+        EXPECTED_COMPLETIONS = [" and I'm going to take a walk.\n\nI really enjoy the scenery, and I'", ", green, yellow, orange, purple, brown, black, white, gray.\n\nI'"]  # fmt: skip
+        self.assertEqual(output_text, EXPECTED_COMPLETIONS)

tests/models/paligemma2/test_modeling_paligemma2.py

@@ -16,6 +16,7 @@
 
 import unittest
 
+import pytest
 from parameterized import parameterized
 
 from transformers import (
@@ -351,6 +352,7 @@ def test_beam_search_low_memory(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip("Gemma2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass
@@ -359,6 +361,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
     def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
         pass
 
+    @pytest.mark.generate
     @unittest.skip("Gemma2 has HybridCache which is not compatible with assisted decoding")
     def test_assisted_decoding_sample(self):
         pass

tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

@@ -16,6 +16,8 @@
 
 import unittest
 
+import pytest
+
 from transformers import AutoModelForCausalLM, AutoTokenizer, RecurrentGemmaConfig, is_torch_available, set_seed
 from transformers.testing_utils import (
     require_bitsandbytes,
@@ -375,6 +377,7 @@ def test_model_parallelism(self):
     def test_model_parallel_beam_search(self):
         pass
 
+    @pytest.mark.generate
     @unittest.skip(reason="Rely on `past_key_values` to crop the assistant pkv. Not supported")
     def test_assisted_decoding_matches_greedy_search(self):
         pass
@@ -383,6 +386,7 @@ def test_assisted_decoding_matches_greedy_search(self):
     def test_left_padding_compatibility(self):
         pass
 
+    @pytest.mark.generate
     @unittest.skip(reason="Relies on `past_key_values` returned by the model. Not supported with recurrent gemma")
     def test_assisted_decoding_sample(self):
         pass

tests/models/smolvlm/test_modeling_smolvlm.py

@@ -423,6 +423,7 @@ def test_eager_matches_sdpa_generate(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
     @unittest.skip(reason="Cache position is off by one leaving out image tokens, FIXME raushan")
     def test_assisted_decoding_matches_greedy_search(self, assistant_type):
         pass